Description: Prerequi- site(s): CS 141, STAT 155. Covers methods for repre- senting and reasoning about probability distributions in complex domains. Focuses on graphical models and their extensions such as Bayesian networks, Markov networks, hidden Markov models, and dy- namic Bayesian networks. Topics include algorithms for probabilistic inference, learning models from data, and decision making.
Credit: May be taken Satisfactory (S) or No Credit (NC) with consent of instructor and graduate advisor.